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CHANGELOG.md

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Notes on versioning

[Unreleased]

Fixes and improvements

0.8.2 (2019-02-16)

  • Update documentation and Library example
  • Revamp args
  • Bug fixes, save moving average in FP32
  • Allow FP32 inference for FP16 models

0.8.1 (2019-02-12)

  • Update documentation
  • Random sampling scores fixes
  • Bug fixes

0.8.0 (2019-02-09)

  • Many fixes and code cleaning thanks @flauted, @guillaumekln
  • Datasets code refactor (thanks @flauted) you need to r-preeprocess datasets

New features

  • FP16 Support: Experimental, using Apex, Checkpoints may break in future version.
  • Continuous exponential moving average (thanks @francoishernandez, and Marian)
  • Relative positions encoding (thanks @francoishernanndez, and Google T2T)
  • Deprecate the old beam search, fast batched beam search supports all options

0.7.2 (2019-01-31)

  • Many fixes and code cleaning thanks @bpopeters, @flauted, @guillaumekln

New features

  • Multilevel fields for better handling of text featuer embeddinggs.

0.7.1 (2019-01-24)

  • Many fixes and code refactoring thanks @bpopeters, @flauted, @guillaumekln

New features

  • Random sampling thanks @daphnei
  • Enable sharding for huge files at translation

0.7.0 (2019-01-02)

  • Many fixes and code refactoring thanks @benopeters
  • Migrated to Pytorch 1.0

0.6.0 (2018-11-28)

  • Many fixes and code improvements
  • New: Ability to load a yml config file. See examples in config folder.

0.5.0 (2018-10-24)

  • Fixed advance n_best beam in translate_batch_fast
  • Fixed remove valid set vocab from total vocab
  • New: Ability to reset optimizer when using train_from
  • New: create_vocabulary tool + fix when loading existing vocab.

0.4.1 (2018-10-11)

  • Fixed preprocessing files names, cleaning intermediary files.

0.4.0 (2018-10-08)

  • Fixed Speech2Text training (thanks Yuntian)

  • Removed -max_shard_size, replaced by -shard_size = number of examples in a shard. Default value = 1M which works fine in most Text dataset cases. (will avoid Ram OOM in most cases)

0.3.0 (2018-09-27)

  • Now requires Pytorch 0.4.1

  • Multi-node Multi-GPU with Torch Distributed

    New options are: -master_ip: ip address of the master node -master_port: port number of th emaster node -world_size = total number of processes to be run (total GPUs accross all nodes) -gpu_ranks = list of indices of processes accross all nodes

  • gpuid is deprecated See examples in https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/FAQ.md

  • Fixes to img2text now working

  • New sharding based on number of examples

  • Fixes to avoid 0.4.1 deprecated functions.

0.2.1 (2018-08-31)

Fixes and improvements

  • First compatibility steps with Pytorch 0.4.1 (non breaking)
  • Fix TranslationServer (when various request try to load the same model at the same time)
  • Fix StopIteration error (python 3.7)

New features

  • Ensemble at inference (thanks @Waino)

0.2 (2018-08-28)

improvements

  • Compatibility fixes with Pytorch 0.4 / Torchtext 0.3
  • Multi-GPU based on Torch Distributed
  • Average Attention Network (AAN) for the Transformer (thanks @francoishernandez )
  • New fast beam search (see -fast in translate.py) (thanks @guillaumekln)
  • Sparse attention / sparsemax (thanks to @bpopeters)
  • Refactoring of many parts of the code base:
  • change from -epoch to -train_steps -valid_steps (see opts.py)
  • reorg of the logic train => train_multi / train_single => trainer
  • Many fixes / improvements in the translationserver (thanks @pltrdy @francoishernandez)
  • fix BPTT

0.1 (2018-06-08)

First and Last Release using Pytorch 0.3.x